Senior Data Engineer (m/f/d)

As a Senior Data Engineer at SoSafe, your main responsibilities include designing, building, and maintaining data pipelines to support business growth, collaborating with product and analytics teams for data-driven solutions, and ensuring the reliability and performance of the data infrastructure. You will also lead efforts to introduce best practices and work on making data accessible through secure delivery mechanisms.

To qualify for the Senior Data Engineer role at SoSafe, you need at least 5 years of experience in building and maintaining end-to-end data pipelines, proficient knowledge of Python, SQL, and data engineering tools such as DMS, Airflow, and dbt. Experience with AWS, DevOps practices, data quality and governance, and excellent communication skills are also important.

SoSafe is committed to the professional growth of its team members, including Senior Data Engineers. With a culture that encourages continuous learning through initiatives like a learning and development allowance, opportunities for collaboration across departments, and participation in technical discussions, you’ll find ample chances to enhance your skills and advance your career.

At SoSafe, work-life balance is prioritized for all team members, including Senior Data Engineers. The company offers flexible hours, 33 vacation days, and a supportive work environment with various mobility and wellness benefits, ensuring that you can maintain a healthy work-life synergy while pursuing your professional goals.

While applying for the Senior Data Engineer position at SoSafe, it’s crucial to be familiar with AWS for cloud infrastructure, Python and SQL for data manipulation, and tools like Airflow and dbt for data pipeline management. Additionally, understanding of DevOps practices, especially with tools like Terraform and familiarity with data governance and quality practices, will be beneficial.

When answering this question, describe a specific project where you faced challenges such as data integration issues or performance bottlenecks. Highlight the steps you took to resolve these issues and the impact of your solutions on the project’s outcome.

Discuss the monitoring tools and practices you use to ensure data pipeline reliability, such as alerting mechanisms, CI/CD practices, and regular audits of data quality. Provide examples of how these practices have helped you maintain data integrity in previous roles.

Explain your approach to continuous learning, including resources you follow, communities you engage with, and any relevant training or certifications you pursue. Mention how you apply new knowledge to benefit your team's projects.

Share your experience in translating complex technical concepts for non-technical stakeholders. Mention specific strategies such as using visual aids, simplifying terminology, and focusing on how data solutions align with business objectives.

Provide examples of event-driven architectures you have implemented or worked with. Discuss the benefits these architectures offer in real-time data processing and how they impact overall system performance and scalability.

List the specific tools you’ve used for monitoring data pipeline performance, such as Grafana, Prometheus, or custom solutions. Explain how these tools help you identify bottlenecks and ensure overall pipeline efficiency.

Talk about your hands-on experience with CI/CD practices, detailing how you’ve integrated these into your data engineering workflow. Provide examples of how CI/CD helped streamline deployments and reduce downtime.

Discuss key considerations for data security and governance, including compliance with regulations, data access controls, and practices to maintain data integrity. Provide examples from your experience where you have addressed these challenges.

Describe your method for prioritizing tasks, possibly using frameworks like the Eisenhower Matrix. Share how you balance urgent and important tasks to ensure timely delivery of projects without compromising quality.

Explain your troubleshooting process step-by-step, including identifying the root cause, isolating the problematic area, and implementing a fix. Describe how you document the process for future reference and learning.